Contexte

Avec la variable continue du CO2 élevé, on se dit que les expressions différentielles deux à deux auront moins d’intérêt que des analyses par splines. L’objective est donc de déterminer les profils des gènes différentiellements exprimés sur la base d’une interpolation polynomiale liée à l’élévation du CO2.

Import et normalisation des données

Données après correction des mix-up samples :

corrplot(cor(data), method = 'color')

tcc <- normalize(data, norm_method = 'tmm', iteration = FALSE)
conditions <- str_split_fixed(colnames(data), '_', 2)[,1]
tcc <- filter_low_counts(tcc, 10*length(conditions))
normalized_counts <- TCC::getNormalizedData(tcc)
draw_distributions(data) /
draw_distributions(normalized_counts)

draw_PCA(normalized_counts)

Differential expression analysis

DF <- 2
FDR <- 0.01

CO2 <- as.numeric(str_split_fixed(conditions, '\\.', 2)[,1])
CO2_spline <- ns(CO2, df = DF)

N <- str_split_fixed(conditions, '\\.', 2)[,2]
design <- model.matrix(~CO2_spline*N)

dge <- edgeR::DGEList(
    counts = normalized_counts,
    norm.factors = rep(1,ncol(normalized_counts)))
  
y <- edgeR::estimateDisp(dge, design)
fit <- edgeR::glmQLFit(y, design)

CO2_coeffs <- 2:(1+DF)
N_coeff <- DF+2
CO2_N_coeffs <- (DF+3):ncol(design)

annot <- gene_annotations$`Arabidopsis thaliana`

CO2 DEGs

CO2_dea <- edgeR::glmQLFTest(fit, coef = CO2_coeffs)$table %>%
  filter(PValue < FDR)

CO2_dea[,c("label", "description")] <- 
  annot[rownames(CO2_dea), c("label", "description") ]

CO2_dea[c("label", "description", "PValue", "logCPM", "logFC.CO2_spline1", "logFC.CO2_spline2")] %>%
  rename(CO2_spline1 = logFC.CO2_spline1, 
         CO2_spline2 = logFC.CO2_spline2) %>%
DT::datatable(
              options = list(scrollX = TRUE)) %>%
  DT::formatSignif(columns = c("PValue", "logCPM",  "CO2_spline1",  "CO2_spline2"),
                   digits = 4)%>%
  DT::formatStyle(
          columns = c("CO2_spline1"),
          target = c("cell", "row"),
          color = DT::styleInterval(cuts = c( 0 ), 
                                              values = c("#FF000035", "#72F02466")))%>%
  DT::formatStyle(
          columns = c("CO2_spline2"),
          target = c("cell", "row"),
          color = DT::styleInterval(cuts = c( 0 ), 
                                              values = c("#FF000035", "#72F02466")))
DEGs_CO2 <- rownames(CO2_dea)

N DEGs

N_dea <- edgeR::glmQLFTest(fit, coef = N_coeff)$table %>%
  filter(PValue < FDR)

N_dea[,c("label", "description")] <- 
  annot[rownames(N_dea), c("label", "description") ]


DT::datatable(N_dea[c("label", "description", "PValue", "logCPM", "logFC")], 
              options = list(scrollX = TRUE)) %>%
  DT::formatSignif(columns = c("PValue", "logCPM",  "logFC"),
                   digits = 4)%>%
  DT::formatStyle(
          columns = c("logFC"),
          target = c("cell", "row"),
          color = DT::styleInterval(cuts = c( 0 ), 
                                              values = c("#FF000035", "#72F02466")))
DEGs_N <- rownames(N_dea)

CO2*N DEGs

CO2_N_dea <- edgeR::glmQLFTest(fit, coef = CO2_N_coeffs)$table %>%
  filter(PValue < FDR)

CO2_N_dea[,c("label", "description")] <- 
  annot[rownames(CO2_N_dea), c("label", "description") ]

CO2_N_dea[c("label", "description", "PValue", "logCPM", "logFC.CO2_spline1.NMix", "logFC.CO2_spline2.NMix")] %>%
  rename(CO2_spline1.NMix=logFC.CO2_spline1.NMix , CO2_spline2.NMix=logFC.CO2_spline2.NMix) %>%
DT::datatable( 
              options = list(scrollX = TRUE)) %>%
  DT::formatSignif(columns = c("PValue", "logCPM",  "CO2_spline1.NMix", "CO2_spline2.NMix"),
                   digits = 4)%>%
  DT::formatStyle(
          columns = c("CO2_spline1.NMix"),
          target = c("cell", "row"),
          color = DT::styleInterval(cuts = c( 0 ), 
                                              values = c("#FF000035", "#72F02466")))%>%
  DT::formatStyle(
          columns = c("CO2_spline2.NMix"),
          target = c("cell", "row"),
          color = DT::styleInterval(cuts = c( 0 ), 
                                              values = c("#FF000035", "#72F02466")))
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
DEGs_CO2_N <- rownames(CO2_N_dea)

Venn diagram of the 3 types of DEGs

draw_venn(list("DEGs_CO2"=DEGs_CO2, "DEGs_N"=DEGs_N, "DEGs_CO2_N"=DEGs_CO2_N))

Profiles of Nitrate genes

ngenes <- read.table('../data/Ngenes.csv', h=T, sep = ';')

genes <- sample(DEGs_CO2, 5)


draw_genes <- function(genes, normalized_counts, ncol=NULL, labels = FASLE){
  
  genes <- intersect(genes, rownames(normalized_counts))
  data <- reshape2::melt(normalized_counts[genes,] , 
               quiet =T, value.name = "Expression") %>%
  rename(gene = Var1, condition = Var2) %>%
  separate(condition, into = c("CO2", "Nutrition", "rep")) %>%
  mutate(CO2 = as.numeric(CO2)) 
  if(labels) {
    data$label <- ngenes[match(data$gene, ngenes$AGI), "Gene"]
    data %>%
    ggplot(aes(x=CO2, y=Expression, color=Nutrition, fill = Nutrition)) +
    geom_point() + geom_smooth(method = "lm", se = TRUE,
                  size = 1, alpha=0.1,
                  formula = y ~ splines::ns(x, df = DF)) +
    facet_wrap(~label, scales = 'free', ncol = ncol) + 
      theme_pubr() + scale_fill_brewer(palette="Accent")+ 
      scale_color_brewer(palette="Accent")+
      labs(title = "Normalized gene expression in CO2 gradient experiment", 
           subtitle = paste("Splines interpolation with DF=", DF))
  }
  else{
    data$label <- ngenes[match(data$gene, ngenes$AGI), "Gene"]
    data %>%
    ggplot(aes(x=CO2, y=Expression, color=Nutrition, fill = Nutrition)) +
    geom_point() + geom_smooth(method = "lm", se = TRUE,
                  size = 1, alpha=0.1,
                  formula = y ~ splines::ns(x, df = DF)) +
    facet_wrap(~gene, scales = 'free', ncol = ncol) + 
      theme_pubr() + scale_fill_brewer(palette="Accent")+ 
      scale_color_brewer(palette="Accent")+
      labs(title = "Normalized gene expression in CO2 gradient experiment", 
           subtitle = paste("Splines interpolation with DF=", DF))
  }
}


draw_genes(ngenes[ngenes$Role == "nitrate uptake transport and metabolism","AGI"], normalized_counts, ncol = 4, labels = T)

draw_genes(ngenes[ngenes$Role == "negative regulator","AGI"], normalized_counts, ncol = 4, labels = T)

draw_genes(ngenes[ngenes$Role == "positive regulator","AGI"], normalized_counts, ncol = 3, labels = T)/
draw_genes(ngenes[ngenes$Role == "signalling","AGI"], normalized_counts, ncol = 3, labels = T)

Ontology enrichment of DEGs

CO2 DEGs

bg <- convert_from_agi(rownames(normalized_counts))
## 
entrez <- convert_from_agi(DEGs_CO2)
go_CO2 <- enrich_go(entrez, bg)
## 
## Registered S3 method overwritten by 'ggforce':
##   method           from 
##   scale_type.units units
# DT::datatable(go_CO2,
#                   options = list(scrollX = TRUE)) 
draw_enrich_go(go_CO2)

Nitrate DEGs

entrez <- convert_from_agi(DEGs_N)
go_N <- enrich_go(entrez, bg)
# DT::datatable(go_N,
#                   options = list(scrollX = TRUE))
draw_enrich_go(go_N)

CO2*N DEGs

entrez <- convert_from_agi(DEGs_CO2_N)
go_CO2_N <- enrich_go(entrez, bg)
# DT::datatable(go_CO2_N,
#                   options = list(scrollX = TRUE))
draw_enrich_go(go_CO2)

Clustering of DEGs

draw_clusters <- function(normalized_counts, genes, clustering, ncol=NULL){
  reshape2::melt(normalized_counts[genes,], 
               quiet =T, value.name = "Expression") %>%
  rename(gene = Var1, condition = Var2) %>%
  separate(condition, into = c("CO2", "Nutrition", "rep")) %>%
  mutate(CO2 = as.numeric(CO2),
         cluster = clustering$membership[gene]) %>%
  group_by(gene) %>%
  mutate(Expression= (Expression-mean(Expression))/sd(Expression)) %>%
    group_by(cluster, CO2, Nutrition, rep) %>%
    mutate(N_genes = n()) %>%
    unite(Cluster_label, cluster, N_genes, sep = ', N=') %>%
  ggplot(aes(x=CO2, y=Expression, color=Nutrition, fill = Nutrition)) +
  geom_point(alpha=0.01)+geom_smooth(method = "lm", se = TRUE,
                size = 1, alpha=0.1,
                formula = y ~ splines::ns(x, df = DF)) +
  facet_wrap(~Cluster_label, scales = 'free', ncol = ncol) + 
    theme_pubr() +scale_fill_brewer(palette="Accent")+ 
    scale_color_brewer(palette="Accent")+
    labs(title = "Normalized clusters expression in CO2 gradient experiment", 
         subtitle = paste("Splines interpolation with DF=", DF))
}

draw_clusters(normalized_counts, DEGs_CO2, 
              run_coseq(conds = conditions, data = normalized_counts, 
                        genes = DEGs_CO2, K = 6, transfo = "arcsin", 
                        model = "Normal", seed = 123)) + ggtitle("C02 DEGs : clusters profiles")
## ****************************************
## coseq analysis: Normal approach & arcsin transformation
## K = 6 to 6 
## Use seed argument in coseq for reproducible results.
## ****************************************

draw_clusters(normalized_counts, DEGs_N, 
              run_coseq(conds = conditions, data = normalized_counts, 
                        genes = DEGs_N, K = 4, transfo = "arcsin", 
                        model = "Normal", seed = 123)) + ggtitle("N DEGs : clusters profiles")
## ****************************************
## coseq analysis: Normal approach & arcsin transformation
## K = 4 to 4 
## Use seed argument in coseq for reproducible results.
## ****************************************

draw_clusters(normalized_counts, DEGs_CO2_N, 
              run_coseq(conds = conditions, data = normalized_counts, 
                        genes = DEGs_CO2_N, K = 9, transfo = "arcsin", 
                        model = "Normal", seed = 123)) + ggtitle("N*CO2 DEGs : clusters profiles")
## ****************************************
## coseq analysis: Normal approach & arcsin transformation
## K = 9 to 9 
## Use seed argument in coseq for reproducible results.
## ****************************************